Leading AI Projects

Deliver AI that works in production — not demos that die in a pilot folder.

Leading AI Projects is hands-on AI project management training for people who must manage AI projects from problem to outcome: scope, data reality, build / buy / integrate, evaluation, human-in-the-loop, and how to operationalize AI inside the company’s delivery method (agile, hybrid, or waterfall).

This is AI project manager training for real AI delivery — including generative AI projects, AI agents, and multi-step agentic workflows, with clear value and control points. You lead the project and equip PMs / delivery leads who execute under your guidance.

Sometimes project context is enough. Sometimes the initiative needs a shared ontology so agents, data, and stakeholders share one meaning model. You will decide which path fits this project — and how that choice shapes the AI project lifecycle.

Instructor: Yevhen Musiienko (Eugene Musienko), PMP
Duration: 2 days
Participants: 8–16
Language: English, Ukrainian, or Russian

Target Audience

  • Project managers and delivery leads who manage AI projects or AI-enabled work
  • Product owners / product managers shipping AI features or workflows
  • Scrum Masters and agile coaches supporting AI delivery on the ground
  • Business analysts and domain experts framing AI problem statements
  • Team leads in engineering, data, operations, or knowledge work
  • Anyone accountable for taking an AI pilot to production under real constraints

Project delivery experience expected. Deep ML engineering not required.

Knowledge and Skills Acquired

Participants will understand:

  • Why AI initiatives fail for management reasons more often than model reasons
  • How to separate hype from fit before building
  • When project context is enough vs when you must build an ontology
  • AI agents and multi-step agentic workflows — value and control points
  • Ethics, HITL, and evaluation as part of responsible AI delivery
  • How to plug AI work into agile, hybrid, or waterfall

Participants will be able to:

  • Write an outcome-based AI project charter (metric + baseline + target + date)
  • Design a core AI workflow (not only personal AI per role)
  • Define HITL points, data boundaries, and stop rules
  • Plan evaluation: quality + business value + AI risk management
  • Set metrics and a keep / pivot / stop rhythm across the AI project lifecycle
  • Run a short pilot cycle and report evidence to sponsors
  • Hand off to operations — operationalize AI with ownership and monitoring basics

Main Topics

Module 1: Framing the AI Project

  • Outcome sentence instead of tool names
  • Feasibility: AI vs automation vs process fix
  • Scope, MVP, non-goals
  • Stakeholder map and decision rights

Module 2: Designing the Workflow (Core AI)

  • Personal AI on the team vs shared AI workflow
  • AI agents and multi-step agentic workflows (e.g. role-based document review loops)
  • Value points vs control points (logging, permissions, human gates)
  • Build / buy / integrate
  • Fitting into agile, hybrid, or waterfall gates

Module 3: Context, Ontology, Data, and Risk

  • When project context is enough for delivery
  • When to build a domain ontology for agents, knowledge, and evaluation
  • Data readiness without becoming a data scientist
  • Privacy, IP, approved data classes
  • Bias, hallucinations, override rates
  • AI risk management register for the project

Module 4: Tools, Agents, and Governance on the Project

  • Choosing tools for this project’s context
  • Governing agentic AI: permissions, logging, human approval
  • Ethical principles applied to delivery tasks
  • Definition of Done for AI outputs

Module 5: Delivery, Metrics, Operationalize AI

  • Learning cycles (4–8 weeks) and evidence reviews
  • Project metrics templates (documentation, code assist, agentic workflow)
  • From AI pilot to production ownership
  • Workshop: one-page AI project management plan + metric pack

Deliverables

  1. AI Project Charter Template
  2. Workflow Design Canvas (actors, agents, handoffs, human gates)
  3. Metrics Pack (baseline / target / how to measure)
  4. Evaluation & Go-Live Checklist
  5. Certificate of Completion

Optional post-training consultation on a live project.

Related Trainings

Training Focus
Generative AI for Leaders & Executives Executive GenAI literacy and tools
Leading AI Adoption Enterprise AI adoption system
Leading AI Projects (this course) AI project management — one initiative to measurable result

Booking

PMDoc.ua/Contacts · Instructor Yevhen Musiienko: +380 (67) 980-2577 · nitoiti@gmail.com · LinkedIn